scholarly journals A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1420 ◽  
Author(s):  
Zhuhua Hu ◽  
Yiran Zhang ◽  
Yaochi Zhao ◽  
Mingshan Xie ◽  
Jiezhuo Zhong ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Prediction ◽  
Short Term ◽  
Water Quality Prediction ◽  
Term Prediction

An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.

Deep learning for water quality prediction: the application of LSTM model to predict water quality in catchment scale

2021 ◽  
Author(s):  
Amir Sahraei ◽  
Lutz Breuer ◽  
Philipp Kraft ◽  
Tobias Houska
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Mean Squared Error ◽  
Model Performance ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Quality Prediction ◽  
Water Quality Prediction

<p>The prediction of water quality is an efficient way for managing water resources and protecting ecosystems by providing an early warning against water quality deterioration. So far, the classical approach is to predict water quality by the utilization of complex process-based water quality models. However, these models are not easy to set up and require comprehensive input data. The local characteristics, detailed process understandings and eventually data from land users such as farmers are needed, to build up a valid model structure. Such constraints can end up in wrong scientific conclusions ranging from false alarms to unpredicted environmental pollution in practical water monitoring application. Long short-term memory (LSTM) algorithms are known to be able to overcome some of the typical constraints in hydrological model applications. However, their performance in water quality prediction has rarely been explored. In this study, we investigate the ability of a LSTM model to predict the complex, nonlinear behavior of water quality parameters in the Schwingbach Environmental Observatory (SEO), Germany.  We predict weekly nitrogen-nitrate concentrations, weekly stable isotopes of water concentrations (δ<sup>18</sup>O) and daily water temperature in six stream‑ and six groundwater sources with different landuse and hillslope conditions. We use meteorological forcing data and catchment attributes as input variables. To ensure an efficient model performance, we employ a Bayesian optimization approach to optimize the hyperparameters of the LSTM. The model performance is evaluated by the Root Mean Squared Error (RMSE). Our LSTM is robust in capturing the dynamics of the water quality parameters over time. The RMSE for the LSTM performance ranges from 0.27 to 3.38 mg/l, from 0.069 to 0.27 ‰ and from 1.3 to 2.1 °C for nitrogen‑nitrate, δ<sup>18</sup>O and water temperature, respectively. We compare the RMSE with statistical parameters of data. Results confirm that the LSTM is a promising tool for early risk assessment of water quality, particularly in view that only a minimal set of catchment information is needed to gain robust results.</p>

scholarly journals Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7271
Author(s):  
Jian Zhou ◽  
Jian Wang ◽  
Yang Chen ◽  
Xin Li ◽  
Yong Xie
Keyword(s):  
Water Quality ◽  
Transfer Learning ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Method ◽  
Water Area ◽  
Quality Information ◽  
Quality Prediction ◽  
Monitoring Point ◽  
Water Quality Prediction

Water environmental Internet of Things (IoT) system, which is composed of multiple monitoring points equipped with various water quality IoT devices, provides the possibility for accurate water quality prediction. In the same water area, water flows and exchanges between multiple monitoring points, resulting in an adjacency effect in the water quality information. However, traditional water quality prediction methods only use the water quality information of one monitoring point, ignoring the information of nearby monitoring points. In this paper, we propose a water quality prediction method based on multi-source transfer learning for a water environmental IoT system, in order to effectively use the water quality information of nearby monitoring points to improve the prediction accuracy. First, a water quality prediction framework based on multi-source transfer learning is constructed. Specifically, the common features in water quality samples of multiple nearby monitoring points and target monitoring points are extracted and then aligned. According to the aligned features of water quality samples, the water quality prediction models based on an echo state network at multiple nearby monitoring points are established with distributed computing, and then the prediction results of distributed water quality prediction models are integrated. Second, the prediction parameters of multi-source transfer learning are optimized. Specifically, the back propagates population deviation based on multiple iterations, reducing the feature alignment bias and the model alignment bias to improve the prediction accuracy. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experimental results demonstrate that the proposed method can make full use of the water quality information of multiple nearby monitoring points to train several water quality prediction models and reduce the prediction bias.

scholarly journals Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1273
Author(s):  
Jianzhuo Yan ◽  
Jiaxue Liu ◽  
Yongchuan Yu ◽  
Hongxia Xu
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Prediction Algorithm ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Quality Prediction ◽  
Water Quality Data ◽  
Water Quality Prediction

The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.

scholarly journals A Long-Term Water Quality Prediction Method Based on the Temporal Convolutional Network in Smart Mariculture

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2907
Author(s):  
Yuexin Fu ◽  
Zhuhua Hu ◽  
Yaochi Zhao ◽  
Mengxing Huang
Keyword(s):  
Water Quality ◽  
Prediction Method ◽  
Quality Parameters ◽  
Quality Data ◽  
Convolutional Network ◽  
Water Quality Data ◽  
Water Quality Prediction ◽  
Term Prediction ◽  
Long Term Prediction

In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic and changeable environment in open waters, but also have many problems, such as poor accuracy, high time complexity and poor long-term prediction. To solve these deficiencies, a new water quality prediction method based on TCN (temporal convolutional network) is proposed to predict dissolved oxygen, water temperature, and pH. The TCN prediction network can extract time series features and in-depth data features by introducing dilated causal convolution, and has a good effect of long-term prediction. At the same time, it is predicted that the network can process time series data in parallel, which greatly improves the time throughput of the model. Firstly, we arrange the 23,000 sets of water quality data collected in the cages according to time. Secondly, we use the Pearson correlation coefficient method to analyze the correlation information between water quality parameters. Finally, a long-term prediction model of water quality parameters based on a time domain convolutional network is constructed by using prior information and pre-processed water quality data. Experimental results show that long-term prediction method based on TCN has higher accuracy and less time complexity, compared with RNN (recurrent neural network), SRU (simple recurrent unit), BI-SRU (bi-directional simple recurrent unit), GRU (gated recurrent unit) and LSTM (long short-term memory). The prediction accuracy can reach up to 91.91%. The time costs of training model and prediction are reduced by an average of 64.92% and 7.24%, respectively.

A Hierchcial Bayesian Model for Spatio-Temporal Water Quality Modeling in a Chainging Climate in South Korea

2020 ◽  
Author(s):  
Minkyu Jung ◽  
Hong-Geun Choi ◽  
Dinh Huy Nguyen ◽  
Hyun-Han Kwon
Keyword(s):  
Water Quality ◽  
South Korea ◽  
Bayesian Model ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Prediction ◽  
Hierarchical Bayesian ◽  
Modeling Framework ◽  
Water Quality Prediction ◽  
Spatio Temporal

<p>Contaminants that cause water pollution are generated from large areas and flow into rivers. It becomes difficult to obtain an accurate prediction of water quality due to the large spatio-temporal variability in a changing climate which in turn leads to considerable uncertainty in the estimation of water quality. Water quality over South Korea highly depends on hydrometeorological conditions due to distinct seasonality. In this context, we explored the use of hydrometeorological variables (i.e., precipitation and temperature) and the autocorrelation structure of water quality parameters in the water quality prediction model within a Bayesian modeling framework. More specifically, we analyzed explored the interdepedencies and correlations between hydrometeorological factors and the water quality parameters for the Mangyeong River basin, and built a hierarchical Bayesian regression model for the TN and TP which are main water quality paramters in South Korea. The result shows that the proposed modeling framework can capture the key aspects of the water quality paramters in terms of seasonality and their uncertainty.</p><p> </p><p>KEYWORDS: Hierarchical Bayesian Model, Meteorological factors, Water Quality prediction</p><p> </p><p>Acknowledgement</p><p>This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI(KMI2018-01215)</p>

scholarly journals Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wei Chen ◽  
Xiao Hao ◽  
JianRong Lu ◽  
Kui Yan ◽  
Jin Liu ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Real Time ◽  
Monitoring System ◽  
Water Environment ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Environment Monitoring ◽  
Long Distance ◽  
Internet Of Things Technology

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.

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